Summary of Toward Guidance-free Ar Visual Generation Via Condition Contrastive Alignment, by Huayu Chen et al.
Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment
by Huayu Chen, Hang Su, Peize Sun, Jun Zhu
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Classifier-Free Guidance (CFG) technique is crucial for enhancing the sample quality of visual generative models. However, when applied to autoregressive (AR) multi-modal generation, CFG introduces design inconsistencies between language and visual content, contradicting the unifying philosophy of different modalities in AR. To address this, we propose Condition Contrastive Alignment (CCA), a novel method that fine-tunes pre-trained models to fit the same distribution target without altering the sampling process. CCA significantly enhances guidance-free performance, on par with guided sampling methods, using just one epoch of fine-tuning on the pretraining dataset. Additionally, CCA achieves trade-offs between sample diversity and fidelity by adjusting training parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual generative models can create high-quality images or videos without human intervention. To make them better, a technique called Classifier-Free Guidance (CFG) is used. But when this technique is applied to multi-modal generation, it doesn’t work well because language and visual content don’t match. Researchers came up with an idea called Condition Contrastive Alignment (CCA). CCA helps pre-trained models fit the same target without changing how they generate samples. This makes guidance-free performance better, similar to using guided sampling methods. |
Keywords
» Artificial intelligence » Alignment » Autoregressive » Fine tuning » Multi modal » Pretraining
